Abstract
Software has played an essential role in engineering, economic development, stock market growth and military applications. Mature software industry count on highly predictive software effort estimation models. Correct estimation of software effort lead to correct estimation of budget and development time. It also allows companies to develop appropriate time plan for marketing campaign. Now a day it became a great challenge to get these estimates due to the increasing number of attributes which affect the software development life cycle. Software cost estimation models should be able to provide sufficient confidence on its prediction capabilities. Recently, Computational Intelligence (CI) paradigms were explored to handle the software effort estimation problem with promising results. In this paper we evolve two new models for software effort estimation using Multigene Symbolic Regression Genetic Programming (GP). One model utilizes the Source Line Of Code (SLOC) as input variable to estimate the Effort (E); while the second model utilize the Inputs, Outputs, Files, and User Inquiries to estimate the Function Point (FP). The proposed GP models show better estimation capabilities compared to other reported models in the literature. The validation results are accepted based Albrecht data set.
Highlights
Estimating software effort on the early stage of development might produce uncertainty of up to 400% as mentioned in [1]
In the 21st century software technology was capable on providing variety of software tools, techniques and software estimation models with many features which can help software project developer, manager, analyst and tester to do their job in a better way
The question that arises according to this opportunity, which tool and which model can really help in providing an accurate estimate? In most cases, the models adopted were based on expert judgment including Delphi technique [3] and work breakdown structure based methods
Summary
Estimating software effort on the early stage of development might produce uncertainty of up to 400% as mentioned in [1]. In the 21st century software technology was capable on providing variety of software tools, techniques and software estimation models with many features which can help software project developer, manager, analyst and tester to do their job in a better way. Practitioners figured out that the inability to correctly estimate software development costs is a challenging problem. Solving this problem becomes a pressure on IT companies since costs associated with their development became higher than before due to software complexity. More research focused on gaining a better understanding of the software development life cycle as well as the intelligent techniques which can help in developing accurate and efficient software cost estimation models. The models should take in consideration the most important attributes which affect the effort modeling process for both the COCOMO and FP models
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